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1. Field of the Invention
The present invention relates generally to a system and method for network management for allocating, selecting and managing resources in an interconnected network and more specifically, to a system and method for network management that uses fuzzy logic principles.
2. Background and Related Art
Networks, consisting of a large number of interconnected points, are formed in many different physical systems. For example, where the points are cities, they may be interconnected by numerous roads and highways, or airline routes, which offer multiple possible pathways to travel from one city to another, or power lines in a large area power grid. The points and interconnecting routes form the network. Nonetheless, what may come to mind first when defining a network is the technological application of the term relating to conduction of an electromagnetic signal between points which are connected by many sections of a conducting medium. The electromagnetic (EM) signal may be electricity or light, which may be conducted through metal wire or cable, or fiber optics, respectively, or may be a broadcast EM signal such as radio frequency (RF) or microwave. The points may be anything capable of receiving and/or transmitting a signal, for example, for purposes of communication or power transmission. In any such network, differences may exist within the interconnections which indicate that one route or pathway may be more desirable than others which would still be capable of achieving the desired transmission. Given the large number of choices offered by the network, a problem arises in managing the transmission from one point to another to handle the signal in the most economical, reliable and/or efficient manner.
In the area of communications, increased demands are rapidly approaching, if not already exceeding, the limits of existing communications networks, both in the areas of bandwidth and speed, particularly in many municipalities and other regions that have communications systems that are many decades old. Expandability of these networks also introduces a significant limitation. Most existing local exchanges rely on electronic transmission of analog signals, typically with separate networks for voice and/or data, and video. As new fiber optic communications networks are being installed, bandwidth, speed and expandability are greatly improved, however, much of the management of these networks is still based on principles established for older wire-based analog systems.
Traditional communications systems are built around a discrete set of subsystems, which, although they may be highly integrated, still act as separate parts. For example, a typical PBX (private branch exchange), although interfaced with an external community network, has its own routing protocols and other functional characteristics which may differ from another PBX connected elsewhere to the community network. The external network has a centralized routing layer which determines to which interface an information packet is to be forwarded, requiring that the central layer have knowledge of each specific interface, and further requiring modification of the central layer if a new interface is added. Some workers in the technology have addressed this issue by utilizing object-oriented methodology, by which the interfaces become "objects", each being encapsulated so that it knows how to receive and what to do with the information packet, independent of the community network. Examples of such systems are provided in U.S. Pat. No. 5,455,854 of Dilts, et al., entitled "Object-Oriented Telephony System", and U.S. Pat. No. 5,509,123 of Dobbins, et al., entitled "Distributed Autonomous Object Architectures for Network Layer Routing", the disclosures of which are incorporated herein by reference.
While incorporation of object-oriented methodology into a network's operations addresses the problems of interfacing for purposes of access and remote management, the operations still rely upon computer control systems which operate on (1) discrete packets of data handled serially (data being defined as any information signal conveyed through the network, including voice, data and video); (2) a memory system which records data to be processed, a sequence of specific instructions on how to process the data, and the answers to that result; and 3) the system operates by a continuous cycle of fetching an instruction from memory (along with any necessary data items), executing that instruction, and storing the result of the instruction, if any, back into memory. In essence, the network operates like a computer having a von Neumann architecture. For example, suppose a telecommunications network has a number of different paths by which a telephone call can be placed between Syracuse, N.Y. and San Diego, Calif. The control system will have in its memory a list of preferred paths, which typically ranges from the shortest distance to the greatest distance between two points, e.g., "least cost routing", or will have in its memory an algorithm which determines, at any given time, the lowest cost path. Path length is easily quantifiable, and the system controller needs only to find the lowest number. If that preferred path is unavailable, it will proceed to the next longer path, under the assumption that the next longer path will be the next lower cost. This assumption is valid since the shortest route usually contains the least amount of costly equipment.
However, the cost of using any given path is not as simple as mere comparison of distances to find the shortest path. Capacity versus existing customers on a particular path, i.e., loading, can make it very expensive, since increased usage of that path ultimately requires expansion. Performance and reliability are among other issues that can effect the choice of paths. In order for the above system controller to consider all of the possible variables that impact cost of a particular path, a complicated algorithm would be required, which would have to applied serially to each of the many possible paths and variables, and could produce a large number of solutions. The network speed would be severely impacted by the delay while the centralized controller considered all of the possible paths, which would further make the tasks of monitoring traffic and status, billing, and service activation, among others, more of a challenge.
Numerous networks and network-like systems which have many alternative "paths" available present similar problems in decision-making processes for allocation of resources, e.g., selection of paths. Electrical power distribution is one example, especially in view of future deregulation of power companies that will open up the industry to competition such that customers can select their own power company, and interconnecting networks of power transmission lines are accessed by many different carriers. An example of a network-like system which has many possible paths and variables is an airline reservation system and process.
Computer systems based upon neural network architecture are known for their uses in character recognition and other organizational and classification functions. Neural networks are composed of a number of simple processing elements that communicate through a set of interconnections with variable weights and strengths. The processing elements are generally grouped into layers, with an input layer, an output layer, and one or more middle layers. The network's memory is stored or represented in the pattern of variable interconnection weights among the processing elements. Information is processed by a spreading, constantly changing pattern of activity distributed across many processing elements. A neural network is trained rather than programmed. The training program adjusts the weights assigned to the inputs to the processing element to control, giving more weight to those elements which provide the best results, i.e., the most correct answer to a set of input training data for which the answer is known. The operation of the neural network is governed by three properties: (1) the transfer function of the processing elements, (2) the details of the structure of the connections among the processing elements, and (3) the learning rules that the system follows. The logic used by a neural network is often referred to as "fuzzy logic" since it deals with probabilities rather than mathematical certainties.
In view of the disadvantages of management by serial processing in a conventional network, even when the network is based upon object-oriented principles, it would be advantageous to devise a communications network architecture and management method which utilized the parallel, high speed capabilities of a neural network. It is to such an object that the present invention is directed.